Yu Zhou, Li Gang, Xu Wanxiu
Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China.
College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China.
Front Oncol. 2024 Sep 11;14:1460136. doi: 10.3389/fonc.2024.1460136. eCollection 2024.
Colorectal cancer (CRC) is one of the most common malignancies, with liver metastasis being its most common form of metastasis. The diagnosis of colorectal cancer liver metastasis (CRCLM) mainly relies on imaging techniques and puncture biopsy techniques, but there is no simple and quick early diagnosisof CRCLM.
This study aims to develop a method for rapidly detecting the risk of liver metastasis in CRC patients through blood test indicators based on machine learning (ML) techniques, thereby improving treatment outcomes. To achieve this, blood test indicators from 246 CRC patients and 256 CRCLM patients were collected and analyzed, including routine blood tests, liver function tests, electrolyte tests, renal function tests, glucose determination, cardiac enzyme profiles, blood lipids, and tumor markers. Six commonly used ML models were used for CRC and CRCLM classification and optimized by using a feature selection strategy.
The results showed that AdaBoost algorithm can achieve the highest accuracy of 89.3% among the six models, which improved to 91.1% after feature selection strategy, resulting with 20 key markers.
The results demonstrate that the combination of machine learning techniques with blood markers is feasible and effective for the rapid diagnosis of CRCLM, significantly im-proving diagnostic ac-curacy and patient prognosis.
结直肠癌（CRC）是最常见的恶性肿瘤之一，肝转移是其最常见的转移形式。结直肠癌肝转移（CRCLM）的诊断主要依靠影像学技术和穿刺活检技术，但目前尚无简单快速的早期诊断方法。
本研究旨在基于机器学习（ML）技术，通过血液检测指标开发一种快速检测CRC患者肝转移风险的方法，从而改善治疗效果。为此，收集并分析了246例CRC患者和256例CRCLM患者的血液检测指标，包括血常规、肝功能、电解质、肾功能、血糖测定、心肌酶谱、血脂和肿瘤标志物。使用六种常用的ML模型对CRC和CRCLM进行分类，并采用特征选择策略进行优化。
结果显示，在六种模型中，AdaBoost算法的准确率最高，为89.3%，采用特征选择策略后提高到91.1%，得到20个关键标志物。
结果表明，机器学习技术与血液标志物相结合对CRCLM的快速诊断是可行且有效的，显著提高了诊断准确性和患者预后。
